Metamaterials
lenovo
2023-05-09
stimuliresponsive materials will be able to react to the stimuli of external physical fields. When stimulated, the metamaterials can automatically deform, make motions, and change their structural properties or functions according to external environments, the changes of the material microstructures when the external field (such as, temperature, external force) continuously applies to the material until a specific condition Strain mismatch refers to the discontinuous changes of strain Due to the different mechanical properties, the uncoordinated strain of each part results in internal stress at the interface under the effect of environmental or load conditions, which furtherly lead to the bending or deformation of the structure. material-instability-based metamaterial design is to controlling the number of the minimum potential energy points of the materials unconstrained homogeneous materials generally yield uniformly expansions or contractions as the temperature rises or falls data-driven methods--unlabeled data to search for undetected patterns of the given dataset rewarding desired behaviors and/or punishing undesired ones rewarding desired behaviors and/or punishing undesired ones map inputs to outputs with data being labeled establish the relation of the input/output parameters match the input data structure required by the selected ML model, and consist of essential material features to ensure high accuracy and training efficiency Gradient-based algorithms require gradient or sensitivity information, in addition to function evaluations, to determine adequate search directions for better designs during optimization iterations. mechanism: properties to create reconfidurable/ Topics Metamaterials Additive M ML Lab Available Material Jetting DESIGN Recommd [3] Machine learning-based inverse design of auxetic metamaterial with zero Poisson's ratio PROPERTY [4] Inverse Design of Inflatable Soft Membranes Through Machine Learning INVERSE DESIGN Origami Kirigami REVIEW APPLICATION REVIEW: stimuli-responsive materials external physical fields heat/Temperature chemicals light field electric current magnetic field pressure action SOFT ROBOT Microfluidics Flexible energy storage materials WEARABLE devices/sensors Bionic gripper. Paper collection phase transition Driving force strain mismatch mechanical instability topology optimization solid phase, liquid phase, and gas phase Important Reviews Optimization materials deployment=>active deformation and controllable response Equipment Others Simulation software Available Material Available Binder Jetting a powder based material and a binder Material Extrusion Powder Bed Fusion(PBF) Sheet Lamination Directed Energy Deposition(DED) VAT Photopolymerisation liquid photopolymer resin+light Fuse deposition modelling (FDM) Direct metal laser sintering (DMLS), Electron beam melting (EBM), Selective heat sintering (SHS), Selective laser melting (SLM) and Selective laser sintering (SLS) PATTERN 1D,2D->3D different expansion coefficients material stimulated by a external (force or thermal) stimuli unconstrained homogeneous materials Material Stimuli 1D,2D->3D->4D different expansion coefficients material stimulated by a external (force or thermal) stimuli unconstrained homogeneous materials Material Stimuli Micro Macro structural instabilities instabilities in microstructured materials Scale multi-stable structures material-instability-based metamaterial design is to controlling the number of the minimum potential energy points of the materials phase transformations domain patterning strain localization STRUCTURAL CURVING BUCKLING TWISTING WRINCLING FOLDING PRESSURE/INDENTATION material instability structural-instability skip the uniform deformation and rapidly jump to the position with low potential energy, Application:Structural Optimization size optimization shape optimization express various mechanics indexes of the structure as a function related to the material distribution, and establish optimization algorithms with constraints to find the optimal solution, and optimize a specific performance of the material. Application method arranging the distribution of the materials to obtain the desired performance of the structure within the specified design domain Analysis Process Thermal-Responsive commercially available/self-assembled 3D printer with different printing methods (i.e., DLP, SLA, FDM, and PolyJet 3D printers in the labs for polymer and composite 3D printing. Have access to metal printing too. stereolithography (SLA) Digital Light Processing (DLP) Chemical-Responsive Light-Responsive Electro-Responsive Magneto-Responsive Pressure-Responsive Pneumatic Actuation Hydraulic Actuation Pros and Cons Functions PRACTICAL Active Shape-Shifting Load Bearing and Impact Protection Elastic Waves Propagation Adjustment Acoustic Stealth Mobility [5] The shell microstructure of the pteropod Creseis acicula is composed of nested arrays of S-shaped aragonite fibers: A unique biological material ML Problem solving Optimization Design Prediction Methods Supervised learning unsupervised learning Reinforcement learning Methods k-means clustering enerative adversarial networks (GANs) semi-supervised learning graph neural networks (GNNs) Methods Methods graph neural networks (GNNs) graph neural networks (GNNs) graph neural networks (GNNs) Methods Support vector machine(SVR) Linear regression/polynomial regression random forest (RF) Feedforward neural network (FFNN); MLP convolutional neural networks(CNNs) Recurrent neural network (RNN); LSTM; GRU Generative adversarial networks (GANs) Methods Gaussian process regression (GPR); Bayesian learning Capture features at different hierarchical levels by calculating convolutions; operate on pixel-based or voxel-based data Charact. structural topology optimization1 Applica. Prediction of strain fields or elastic properties of high-contrast composites Prediction of strain fields or elastic properties of high-contrast composites, modulus of unidirectional composites,stress fields in cantilevered structures, or yield strength of additive-manufactured metals; prediction of fatigue crack propagation in polycrystalline alloys; prediction of crystal plasticity; design of tessellate composites; design of stretchable graphene kirigami; Connect nodes (neurons) forming a directed graph with history information stored in hidden states; operate on sequential data Prediction of fracture patterns in crystalline solids; prediction of plastic behaviors in heterogeneous materials;multi-scale modeling of porous media Train two opponent neural networks to generate and discriminate separately until the two networks reach equilibrium; generate new data according to the distribution of training set Prediction of modulus distribution by solving inverse elasticity problems; prediction of strain or stress fields in composites; composite design; structural topology optimization; architected materials design Treat parameters as random variables and calculate the probability distribution of these variables; quantify the uncertainty of model predictions Modulus or strength prediction; design of supercompressible and recoverable metamaterials Operate on non-Euclidean data structures; applicable tasks include link prediction, node classification and graph classification Hardness prediction;127 architected materials design168 Sub Conditional Generative Adversarial Network (CGAN) Appli. A generative adversarial network (GAN) is a type of deep learning network that can generate data with similar characteristics as the input training data. Defi. generator+discriminator: [inverse design] It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target prop. Appli. membrane inflation+binary material( shape changing capabilities) pre-programmed 3D shapes starting from 2D planar composite membranes Analysis Process Modeling/simulation AM Experimental and numerical validation Summary: Selection: Proper algorithm model Inputs outputs types of materials architectures(micro) Material Property stiffness Database generation: Machine learning prediction Model training model evaluation FEA Inputs/outpus data(2) Application output:generating structural property form flexibility Dataset predict the properties tailor the micro-architectures for metamaterials according to external conditions. literature/existing databases high-throughput experiments FEA-SIMULATION Data resource Data preprocess Computational problems Model Design 1)ML-based Applicable: a well-defined research problem of mechanical materials that has not been addressed by conventional methods, or has been solved but can be outperformed by ML-based approaches. Possible material database small set of dataset ~10% dataset: evaluation ~90% dataset: training data Data order: shuffled laser cutting machine [6] Machine Learning-Evolutionary Algorithm Enabled Design for 4D-Printed Active Composite Structures bilayer composite+stimuli +strain mismatch+4D shape changing design 3D->4D time,3D printed parts to transform their shapes in the 4th dimension Problem [forward problem] predicting shape changes for given material or property distributions [inverse problem] of finding the optimal material or property distribution to obtain the desired shape change. [Simulation] accurate numerical models (or predictive models), incorporating the forward predictive model into some [optimization algorithms]//topology Optimization topology optimization soft actuators Algorithms gradient-free optimization algorithms Gradient-based algorithms evolutionary algorithms Propertyies optimmization Active Mechanical Metamaterials ML 4D PRINTING(Direct Ink Writing Based +appli.) Supporting ref 4D PRINTING Paper structure [Matlab] [DLP+Resin] AM 3D PRINTING Future of additive manufacturing: Overview of 4D and 3D printed smart and advanced materials and their applications 4D PRINTING Topology optimization is an iterative gradient-based design process which minimizes an objective and satisfies a set of selected design constraints by distributing material in a design domain. Define Define Methods Appli. designing certain shape-changing responses of active composites/// other engineering structural problems. Mechanics-based design strategies for 4D printing: A review Polyjet tech Compatible materials Tactile, opaque, flexible, transparent or rigid– the J55™ Prime offers a wide range of materials to suit all your design needs. Multi-material capabilities let you load up to five materials at once and create multi-color or multi shore level parts in one print. With expansive options for color and texture combinations, there’s no need for hand painting. convolutional neural network (CNN) ML relies on numerous iterations of FE simulations to explore a large design space, thus suffering from high computational cost. Pro/Cons because the CNN model cannot predict some complicated designs very well whilst the inverse design problem requires high prediction accuracy Application Soft Actuator A Review of 3D-Printable Soft Pneumatic Actuators and Sensors: Research Challenges and Opportunities A Review of 3D-Printable Soft Pneumatic Actuators and Sensors: Research Challenges and Opportunities thermo-mechanical tester for compression/tesions, torsion, bending analysis testers for measuring thermal conductivity, electrical properties, piezoelectric and pyroelectric coefficients Tester ANSYS COMSOL Abaqus AM Processing [8] Combining advanced 3D printing technologies with origami principles: A new paradigm for the design of functional, durable, and scalable springs Anisotropic compression behaviors of bio-inspired modified body-centered cubic lattices validated by additive manufacturing Interesting Topic SoftPneuActuator+PDMS Soft Pneu Actuator PDMS/Polymer ML Tentative Idea FEA Polymer Hysteresis Soft Pneumatic Actuator-Actuation Constrain AM Modeling Analysis Compliant mechanism Flexible-based structure FEA compliant structure's kinematics and statics pseudo-rigid-body (PRB) model FEA Material Analysis methods FEA FEA Tutorial for Soft Actuator [1] [2] Data-driven foward+inverse ML model Data Acquisition Data preprocess inverse forward Strain mismatch Compliant mechanism+flexible materials Compliant mechanism+flexible materials [Paper] Introducing Mass Parameters to Pseudo-Rigid-Body Models for Precisely Predicting Dynamics of Compliant Mechanisms Analysis methods FEA Dynamics [Paper] Programmable Multistable Perforated Shellular Design Material Property Changing Stiffness? variable stiffness beam concepts as stiffness change unit. Multiple units can be combined to construct variable stiffness Design synthesis of new compliant mechanisms will be conducted based on the modular unit concepts. [Paper] Machine learning-based design and optimization of curved beams for multistable structures and metamaterials different flexible material [Paper]Soft Pneumatic Actuator with Adjustable Stiffness Layers for Multi-DoF Actuation Inspiration [Paper]3D Printing of a Polydimethylsiloxane/ Polytetrafluoroethylene Composite Elastomer and its Application in a Triboelectric Nanogenerator Reference flexible shape changing soft pneumatic actuator Intro. [Paper]Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators [paper] Inverse Design of Inflatable Soft Membranes Through Machine Learning frame constrain structure Posture assessment Forward+inverse PDMS [paper] 3D Printing of a Polydimethylsiloxane/Polytetrafluoroethylene Composite Elastomer and its Application in a Triboelectric Nanogenerator Soft Pneumatic Actuator [paper] A Review of 3D-Printable Soft Pneumatic Actuators and Sensors: Research Challenges and Opportunities Multi-DoF Actuation [paper] Soft Pneumatic Actuator with Adjustable Stiffness Layers for Multi-DoF Actuation Predicting Dynamics of Compliant Mechanisms [Paper] Introducing Mass Parameters to Pseudo-Rigid-Body Models for Precisely Predicting Dynamics of Compliant Mechanisms [paper] A proposed soft pneumatic actuator control based on angle estimation from data-driven model Design of soft multi-material pneumatic actuators based on principal strain field [paper] Position Control for Soft Actuators, Next Steps toward Inherently Safe Interaction Mixting ratio [paper] Mechanical Characterization of PDMS with Different Mixing Ratios Subtopic [paper] Fabrication and Dynamic Modeling of Bidirectional Bending Soft Actuator Integrated with Optical Waveguide Curvature Sensor Modelling Large Deflection of a Compliant Mechanism [paper] Modelling Large Deflection of a Compliant Mechanism: A Comparative Study Using Discrete Euler Beam Constraint Model, Discrete Timoshenko Beam Constrain Model, Finite Element Method and Experiment [12] Knowledge extraction and transfer in data-driven fracture mechanics Our learning framework has the potential to shape future fusion research and tokamak development. Underspecified objectives can find configurations that maximize a desired performance objective or even maximize power production. Our architecture can be rapidly deployed on a new tokamak without the need to design and commission the complex system of controllers deployed today, and evaluate proposed designs before they are constructed. More broadly, our approach may enable the discovery of new reactor designs by jointly optimizing the plasma shape, sensing, actuation, wall design, heat load and magnetic controller to maximize overall performance. Dimensionality reduction Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Truncated Singular Value Decomposition (SVD) Factor Analysis (FA) From Hamid Akbarzadeh, Dr. piezo (mechanical + electrical coupling) [09:24] Hamid Akbarzadeh, Dr. pyroelectric (temperature + electrical coupling) soft material most. If not , used as sensors/actuater DIW directed writing Selective laser sintering (SLS) STRUCTURAL DESIGN MULTI-FUNCTIONAL/Multi-Functionality MATERIAL PROPERTY MECHANICAL PROPERTY Rational design of piezoelectric metamaterials with tailored electro-momentum coupling Analysis and Optimisation of Periodic Piezoelectric Materials Optimization of piezoelectric metamaterials # Multi-objective structural optimisation of piezoelectric materials Piezoelectric Materials Softrobo+Motion control+dielectric elastomer actuators Motion Control of a Soft Circular Crawling Robot via Iterative Learning Control∗ As an actuation technology of soft robots, dielectric elastomer actuators (DEAs) exhibit many fantastic attributes such as large strain and high energy density. Ferroelectricity+AM A 3D-printed molecular ferroelectric metamaterial Ferro Ferroelectricity/https://www.britannica.com/science/ferroelectricity What is the difference between dielectric and ferroelectric? https://www.researchgate.net/post/What_are_the_differences_between_insulator_dielectrics_and_paraelectrics ferroelectric and piezoelectric(direct piezoelectric effect/inverse piezoelectric effect)? Piezoelectricity is a property of certain dielectric materials to physically deform in the presence of an electric field, or conversely, to produce an electrical charge when mechanically deformed. ferroelectricity, property of certain nonconducting crystals, or dielectrics, that exhibit spontaneous electric polarization (separation of the centre of positive and negative electric charge, making one side of the crystal positive and the opposite side negative) that can be reversed in direction by the application of an appropriate electric field. https://www.nrel.gov/materials-science/piezoelectric-ferroelectric-materials.html Ferro Intro Auxetic material Auxetic materials, structures, fabrics (or also “Auxetics”, a term that commonly groups all of them) are materials that exhibit an unexpected behaviour when they are subjected to mechanical stresses and strains. intro. paper ferroelectric metamaterials Tunable ferroelectric auxetic metamaterials for guiding elastic waves in three-dimensions Metamaterials are artificial material systems that can be designed for extraordinary static and dynamic properties, such as negative effective Poisson’s ratio, mass density, or Young’s modulus [1], [2]. Metamaterials have been proposed for numerous applications in controlling sound, vibrations, and heat. Such applications range from wave guiding, cloaking, thermal diodes, energy transfer optimization to acoustic rectifiers [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]. Traditionally, metamaterials designs are fixed, i.e., once fabricated, their effective properties cannot be changed. However, a growing trend in metamaterials’ research is utilizing dynamically tunable designs, thus opening the door for more potential applications and functional integration in devices. Tunability can be achieved through a variety of methods including mechanical (e.g., by considering application of external loads) [18], [19], [20], [21], thermal (e.g., through shape memory effects [22]), electrical (e.g., from nano [23] to macro-scale systems [24], [25], [26]), or magnetic [27], [28], [29] stimuli. While some studies of tunable piezoelectric metamaterials have been reported in the literature [30], [31], [32], [33], [34], [35], harnessing the effects of ferroelectric poling to tune metamaterials properties remains relatively unexplored. Here, we discuss the interplay between different tuning avenues in a three-dimensional metamaterial, namely poling effects and mechanical deformations. Ferroelectric metamaterials PROPERTY ADJUSTABLE stiffness designable metamaterials negative Poisson’s ratio metamaterials negative thermal expansion (NTE) metamaterials negative stiffness energy absorption SHAPE CHANGE/shape morphing extreme mechanical properties TUNABLE/PROGRAMMABLE Twisting for soft intelligent autonomous robot in unstructured environments Environment-responsive soft robots constructed from twisted LCE ribbons with a stra DATA-DRIVEN REVIEW: ML Utilization data collection, generation and preprocessing mechanical property prediction materials design Active learning in materials science Dielectrics Electromagnetic Reconfiguration Using Stretchable Mechanical Metamaterials Papers Tunable thermally bistable multi-material structure Papers 3D Printed Graphene-Based Metamaterials: Guesting Multi- Functionality in One Gain Advanced functional materials with fascinating properties and extended structural design have greatly broadened their applications. Metamaterials, exhibiting unprecedented physical properties (mechanical, electromagnetic, acoustic, etc.), are considered frontiers of physics, material science, and engineering. With the emerging 3D printing technology, the manufacturing of metamaterials becomes much more convenient. Graphene, due to its superior properties such as large surface area, superior electrical/thermal conductivity, and outstanding mechanical properties, shows promising applications to add multi-functionality into existing metamaterials for various applications. In this review, the aim is to outline the latest developments and applications of 3D printed graphene-based metamaterials. The structure design of different types of metamaterials and the fabrication strategies for 3D printed graphene-based materials are first reviewed. Then the representative explorations of 3D printed graphene-based metamaterials and multi-functionality that can be introduced with such a combination are further discussed. Subsequently, challenges and opportunities are provided, seeking to point out future directions of 3D printed graphene-based metamaterials. Stretchable DESIGN METHODS FORWARD DESIGN Multidimentional RECONFIGURATION(configuration/ configurable) 3D Printed Fractal Metamaterials with Tunable Mechanical Properties and Shape Reconfiguration Electromagnetic negative zero Multi-material topology optimization and additive manufacturing for metamaterials incorporating double negative indexes of Poisson’s ratio and thermal expansion [paper] Machine learning-based inverse design of auxetic metamaterial with zero Poisson’s ratio Conformal Conformal elasticity of mechanism-based metamaterials Nonlinear Inverse Design of Mechanical Metamaterials with Target Nonlinear Response via a Neural Accelerated Evolution Strategy Learning the nonlinear dynamics of mechanical metamaterials with graph networks the unique nonlinear dynamics of certain types of soft mechanical metamaterials. However, capturing the nonlinear dynamic response of these materials especially those with complex geometries, can be a challenge due to the strong nonlinearity and large computational cost. An efficient and reliable framework to predict the overall response of the metamaterials based on the geometry of their building blocks is not only key to understanding the unique behavior of metamaterials, but also vital to the rational design of such materials. metamaterial graph network lattice-like metamaterial structure. The Topological invariant and anomalous edge modes of strongly nonlinear systems THERMAL Papers Soft Robotics in Healthcare: Challenges in Design and Control Bistable and Multistable Actuators for Soft Robots: Structures, Materials, and Functionalities MULTISTABLE/BISTABLE Bistable and Multistable Actuators for Soft Robots: Structures, Materials, and Functionalities [Paper] Inverse Design of Mechanical Metamaterials That Undergo Buckling AM METHODS AM ANALYSYS defect influence identify the most important defect and design features that determine the mechanical properties of the overall structure. Machine learning assisted investigation of defect influence on the mechanical properties of additively manufactured architected materials FEA data-driven simulation Magneto-Thermomechanically Reprogrammable Mechanical Metamaterials Magneto-Thermomechanically Reprogrammable Mechanical Metamaterials Magnetorheological Fluid-Based Flow Control for Soft Robots Video actuation methods such as shape-memoryalloys, [7,8]dielectric elastomers, [9]ionicpolymers,[10,11]and hydrogel- based actua-tors,[1 Refer [18]Soft Poly-Limbs: Toward a New Paradigm of Mobile Manipulation for Daily Living Tasks Subtopic shape memory polymers (SMPs) shape-memoryalloys shape memory material FABRICATION COMPOSITE shape memory polymers (SMPs) liquid crystal elastomers (LCEs) hydrogels composites Applica. Difficulties active material involves higher geometric or material nonlinearities (e.g., multiphysics driven material nonlinearity). shape-memoryalloys intro. depend on the spatial distributions of materials or properties